Package 'wcep'

wcep plot

Description

Create a plot of Kaplan-Meier curve with its specified confidence interval

Usage

## S3 method for class 'wcep'
plot(
  x,
  main = " ",
  type = "n",
  lty = NULL,
  lwd = NULL,
  xlab = " ",
  ylab = "Survival Probability",
  xlim = NULL,
  ylim = NULL,
  cex = NULL,
  ...
)

Arguments

x	is an object of class "wcep"
main	title of plot
type	type of plot
lty	line type
lwd	line width
xlab	first axis label
ylab	second axis label
xlim	first axis limits
ylim	second axis limits
cex	legend font size
...	other parameters of generic "plot" have no use here setOldClass("wcep")

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Toy example

Description

A data set containing patient IDs, event types, event times, and gender of 100 patients.

Usage

toyexample

Format

A data frame with 104 rows and 4 columns

PTID

ID number of patients

EvTp

Event Types: SHK as Shock, CHF as Congestive Heart Failure, REMI as Recurrent Myocardial Infarction, DTH as Death; and N as No event

EvTm

Event Time (day)

sex

Gender of patients, M as Male, F as Female

References

Armstrong P. W., Westerhout C. M., Van de Werf F., Califf R. M., Welsh R. C., Wilcox R. G., Bakal J. A. (2011) Refining clinical trial composite outcomes: an application to the Assessment of the Safety and Efficacy of a New Thrombolytic-3 (ASSENT-3) trial. American Heart Journal. 161(5) 848-854.

Source

It is a generated example based on ASSENT-3: https://pubmed.ncbi.nlm.nih.gov/21570513

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Analysis of weighted composite endpoints

Description

Analyze given data frame and return Kaplan-Meier survival probabilities together with the specified confidence interval. wcep modifies Kaplan-Meier curve by taking into account severity weights of different event. Alternative methods are Anderson Gill model and win ratio of composite outcomes.The function takes event dataset and user-specified severity weights to generate a modified Kaplan-Meier curve and comparison statistics based on the weighted composite endpoint method. The user supplies the event data set, the weights, and the factor to split on . The package will generate the weighted survival curve, confidence interval and test the differences between the two groups.

Usage

wcep(x, EW, alpha = 0.05, split = FALSE)

Arguments

x	This data frame usually has 3 columns. The first column specifies patient ID, which is a character or numeric vector, the second column is a factor with character values of event types. The third column is a numeric vector of event times. If split = TRUE, then the forth column is a character vector of split groups of at most two groups, like gender.
EW	This data frame has two columns. The first column specifies a character vector of event types. The second column specify weights. The naming of event types in x and EW should be exactly similar. Events with weight = 1 are terminal events; events with weight = 0 are censoring events. Data should contain at most one terminal event type and one censoring event type. IDs should have at most either one terminal event type or one censoring event type. IDs without either a terminal or censor event listed will be censored at the maximum follow time otherwise listed in the data.
alpha	A numeric value between 0-1 which specifies the confidence level, if it is not specified, by default is 0.05.
split	A logical value of T or F which allows to compare two groups.

References

Bakal J., Westerhout C. M., Armstrong P. W. (2015) Impact of weighted composite compared to traditional composite endpoints for the design of randomized controlled trails. Statistical Methods in Medicine Research. 24(6) 980-988.

Nabipoor M., Westerhout C. M., Rathwell S., Bakal J. (2023) The empirical estimate of the survival and variance using a weighted composite endpoint, BMC Medical Research Methodology. 23(35).

Author(s)

Majid Nabipoor: [email protected], Cynthia Westerhout: [email protected], Jeffrey Bakal: [email protected], Sarah Rathwell: [email protected]

See Also

coxph for Anderson Gill model

Examples

data(toyexample)
#event weights
EW <- data.frame(event = c('CHF','DTH','SHK','REMI','N'), weight = c(0.3,1,0.5,0.2,0))
res1 <- wcep(toyexample, EW)
str(res1)
res1$survival_probabilities
plot(res1)
#comparing two genders
res2 <- wcep(toyexample, EW, split=TRUE)
plot(res2)

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